Unbinned maximum likelihood is a common procedure for parameter estimation.
After parameters have been estimated, it is crucial to know whether the fit
model adequately describes the experimental data. Univariate Goodness of Fit
procedures have been thoroughly analyzed. In multi-dimensions, Goodness of Fit
test powers have rarely been studied on realistic problems. There is no
definitive answer to regarding which method is better. Test performance is
strictly related to specific analysis characteristics. In this work, a review
of multi-variate Goodness of Fit techniques is presented.